Monthly Runoff Prediction Method Based on Secondary Decomposition and SupportVector Machine

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Monthly Runoff Prediction Method Based on Secondary Decomposition and SupportVector Machine

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[1]GAN Rong,MA Chaoxin,GAO Yong,et al.Monthly Runoff Prediction Method Based on Secondary Decomposition and SupportVector Machine[J].Journal of Zhengzhou University (Engineering Science),2024,45(06):32-39.[doi:10.13705/j.issn.1671-6833.2024.06.003]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 06 Page number: 32-39 Column: Public date: 2024-09-25

Title:: Monthly Runoff Prediction Method Based on Secondary Decomposition and SupportVector Machine

Author(s):: GAN Rong¹; 2; MA Chaoxin¹; 2; GAO Yong³; GUO Lin³; HOU Xiaoli⁴; LU Xueyong⁵; 1. School of Water Resources and Transportation, Zhengzhou University, Zhengzhou 450001, China; 2. Henan Key Laboratory of Groundwater Pollution Prevention and Rehabilitation, Zhengzhou 450001, China; 3. Henan Provincial Geological Research Institute,Zhengzhou 450001, China; 4. Henan Province Yudong Water Resources Guarantee Center, Kaifeng 475000, China;5. Canal Head Branch Company of China South-to-North Water Diversion Middle Route Corporation Limited, Nanyang 473000, China

Keywords:: monthly runoff prediction; secondary decomposition; STL; VMD; SVM; neural network

CLC:: P338TV121

DOI:: 10.13705/j.issn.1671-6833.2024.06.003

Abstract:: A monthly runoff prediction model( STL-VMD-SVM) based on a secondary decomposition using loess( STL) and variational mode decomposition (VMD) combined with a support vector machine( SVM) was proposed to address the nonlinear and non-stationary characteristics of runoff sequences. This model utilized STL to decompose the original runoff sequence into seasonal, trend, and residual terms of different frequencies and decomposedthe residual term into IMFs through VMD. An SVM model was established to predict seasonal, trend, and IMFs.The sum of the predicted values of all IMFs was the predicted value of the residual term, and the product of seasonal, trend, and residual terms was the final predicted value of the original runoff series. Based on the monthly runofftime series of Heishiguan Station and Gaocun Station on the mainstream of the Yellow River in the Yiluo River Basin, an example application and universality evaluation were conducted, and compared with the BP neural networkmodel and the long shortterm memory neural network model( LSTM) . The results showed that for the runoff prediction of Heishiguan Station in the Yiluo River Basin, the NSE, MAPE, RMSE, and R in the validation period of theproposed model were 0. 977, 13. 705%, 0. 327 and 0. 991, respectively, and their prediction accuracy was betterthan that of the single model and the primary decomposition model. The secondary decomposition of STL-VMDcould effectively improve the prediction accuracy of the model. The NSE, MAPE, RMSE, and R during the validation period in the runoff prediction at Gaocun Station on the mainstream of the Yellow River were 0. 979, 8. 509%,3. 263, and 0. 989, respectively, which also achieved good prediction results.

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Last Update: 2024-09-29